Improved forest fire spread mapping by developing custom fire fuel models in replanted forests in Hyrcanian forests, Iran

Abstract

Aim of the study:Forest fuel classification and characterization is a critical factor in wildfire management. The main purpose of this study was to develop custom fuel models for accurately mapping wildfire spread compared to standard models.

Area of study: The study was conducted at a replanted forest dominated by coniferous species, in the Arabdagh region,GolestanProvince, northernIran.

Material and methods: Six custom fuel models were developed to characterize the main vegetation types in the study area. Fuel samples were collected from 49 randomly selected plots. In each plot, the fuel load of 1-hr, 10-hr, 100-hr, 1000-hr, live herbs, live woody plants, surface area volume ratio, and fuel depth were estimated using the Fuel Load (FL) sampling method along three transects. Canopy fuel load was calculated for each fuel model. The performance of the custom fuel models versus standard fuel models on wildfire behavior simulations was compared using the FlamMap MTT simulator.

Main results: The results showed that, despite the similarity in the burned area between observed and modeled fires, the custom fuel models produced an increase in simulation accuracy. Compared to the observed fire, simulation results did not give realistic results to the crown fire. The simulation using standard fuel models did not result in crown fire, while the simulation using custom fuel models showed a moderate rate of crown fire with a Kappa coefficient of 0.54.

Research highlights: The results demonstrated the importance of developing custom fuel models to simulate wildfire maps with higher accuracy for wildfire risk management.

Keywords: custom fuel model; FlamMap; replantation; vegetation type; wildfire behavior.

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Author Biography

Shaban Shataee Joibary, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan.
Forestry department, Gorgan University of agricultural sciences and natural resources.

References

Adab H, Atabati A, Oliveira S, Gheshlagh AM, 2018. Assessing fire hazard potential and its main drivers in Mazandaran province, Iran: a data-driven approach. Environ Monit Assess 190: 670. https://doi.org/10.1007/s10661-018-7052-1

Ager AA, Vaillant NM, Finney MA, 2011. Integrating Fire Behavior Models and Geospatial Analysis for Wildland Fire Risk Assessment and Fuel Management Planning. 2011. https://doi.org/10.1155/2011/572452

Albini FA, 1976. Estimating wildfire behavior and effects. Gen Tech Rep INT-GTR-30 Ogden, UT US Dep Agric For Serv Intermt For Range Exp Station 92 p 30.

Alexander ME, Cruz MG, 2013. Are the applications of wildland fire behaviour models getting ahead of their evaluation again? Environ Model Softw 41: 65-71. https://doi.org/10.1016/j.envsoft.2012.11.001

Allard GB, 2001 The Fire Situation in Islamic Republic of Iran; FAO. For Dep Rome, Italy.

Andela N, Morton DC, Giglio L, Paugam R, Chen Y, Hantson S, Van Der Werf GR, Anderson JT, 2019. The Global Fire Atlas of individual fire size, duration, speed and direction. Earth Syst Sci Data 11: 529-552. https://doi.org/10.5194/essd-11-529-2019

Anderson HE, 1981. Aids to determining fuel models for estimating fire behavior (US Department of Agriculture, Forest Service, Intermountain Forest and Range …). https://doi.org/10.2737/INT-GTR-122

Arca B, Bacciu V, Pellizzaro G, Salis M, Ventura A, Duce P, Spano D, Brundu G, 2009. Fuel model mapping by IKONOS imagery to support spatially explicit fire simulators. In 7th International Workshop on Advances in Remote Sensing and GIS Applications in Forest Fire Management towards an Operational Use of Remote Sensing in Forest Fire Management, (Matera), pp. 2-5.

Arca B, Duce P, Laconi M, Pellizzaro G, Salis M, Spano D, 2007. Evaluation of FARSITE simulator in Mediterranean maquis. Int J Wildl Fire 16: 563-572. https://doi.org/10.1071/WF06070

Bennett M, Fitzgerald SA, Parker B, Main ML, Perleberg A, Schnepf C, Mahoney RL, 2010. Reducing fire risk on your forest property. A Pacific Northwest Extension Publication PNW 618. Available at: https://catalog.extension.oregonstate.edu/sites/catalog/files/project/pdf/pnw618.pdf

Burgan RE, 1988. Revisions to the 1978 national fire-danger rating system. Res Pap SE-273 Asheville, NC US Dep Agric For Serv Southeast For Exp Station 144 p 273. https://doi.org/10.2737/SE-RP-273

Burgan RE, Rothermel RC, 1984. BEHAVE: Fire Behavior Prediction and Fuel Modeling System, Fuel Subsystem. United States: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. https://doi.org/10.2737/INT-GTR-167

Brown JK, Oberheu RD, Johnston CM, 1982. Handbook for inventorying surface fuels and biomass in the Interior West. USDA For. Serv. Gen. Tech. Rep. INT-129, 48 p. Intermt For Range Exp Stn, Ogden, Utah 1 84001. https://doi.org/10.2737/INT-GTR-129

Cai L, He HS, Wu Z, Lewis BL, Liang Y, 2014. Development of standard fuel models in boreal forests of northeast China through calibration and validation. PLoS One 9: 1-10. https://doi.org/10.1371/journal.pone.0094043

Calkin DC, Finney MA, Ager AA, Thompson MP, Gebert KM, 2011. Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making. For Policy Econ 13: 378-389. https://doi.org/10.1016/j.forpol.2011.02.007

Carlson JD, Burgan RE, 2003. Review of users' needs in operational fire danger estimation: the Oklahoma example. Int J Remote Sens 24: 1601-1620. https://doi.org/10.1080/01431160210144651

Charrad M, Ghazzali N, Boiteau V, Niknafs A, 2014. Determining the best number of clusters in a data set. J Stat Softw. https://doi.org/10.18637/jss.v061.i06

Chatto K, Tolhurst KG, 2004. A review of the relationship between fireline intensity and the ecological and economic effects of fire, and methods currently used to collect fire data.Department of Sustainability and Environment, Fire Management Branch, research report no. 67, Melbourne, Australia.

Cheyette D, Rupp TS, Rodman S, 2008. Developing fire behavior fuel models for the wildland-urban interface in Anchorage, Alaska. West J Appl For 23: 149-155. https://doi.org/10.1093/wjaf/23.3.149

Cobian-Iñiguez J, Aminfar A, Weise DR, Princevac M, 2019. On the Use of Semi-empirical Flame Models for Spreading Chaparral Crown Fire. Front Mech Eng 5: 1-13. https://doi.org/10.3389/fmech.2019.00050

Congalton RG, Green K, 2002. Assessing the accuracy of remotely sensed data: principles and practices (CRC press).

Conglaton RG, 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Env 37: 35-46. https://doi.org/10.1016/0034-4257(91)90048-B

Cram DS, 2006. Wildland fire effects in silviculturally treated vs. untreated stands of New Mexico and Arizona (USDA, Forest Service, Rocky Mountain Research Station). https://doi.org/10.2737/RMRS-RP-55

Cruz MG, Alexander ME, Fernandes PAM, 2008. Development of a model system to predict wildfire behaviour in pine plantations. Aust For 71: 113-121. https://doi.org/10.1080/00049158.2008.10676278

Cruz MG, Fernandes PM, 2008. Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands. Int J Wildl Fire 17: 194-204. https://doi.org/10.1071/WF07009

Cruz MG, Hurley RJ, Bessell R, Sullivan AL, 2020. Fire behaviour in wheat crops-effect of fuel structure on rate of fire spread. Int J Wildl Fire. https://doi.org/10.1071/WF19139

Dymond CC, Roswintiarti O, Brady M, 2004. Characterizing and mapping fuels for Malaysia and western Indonesia. Int J Wildl Fire 13: 323-334. https://doi.org/10.1071/WF03077

Elia M, Lafortezza R, Lovreglio R, Sanesi G, 2015. Developing Custom Fire Behavior Fuel Models for Mediterranean Wildland-Urban Interfaces in Southern Italy. Environ Manage 56: 754-764.. https://doi.org/10.1007/s00267-015-0531-z

Fernandes PAM, 2001. Fire spread prediction in shrub fuels in Portugal. For Ecol Manage 144(1-3): 67-74. https://doi.org/10.1016/S0378-1127(00)00363-7

Fernandes PM, 2009. Combining forest structure data and fuel modelling to classify fire hazard in Portugal. Ann For Sci 66: 1-9. https://doi.org/10.1051/forest/2009013

Finney MA, 1998. FARSITE: Fire Area Simulator - Model Development and Evaluation. USDA For Serv - Res Pap RMRS 1-36.. https://doi.org/10.2737/RMRS-RP-4

Finney MA, 2002. Fire growth using minimum travel time methods. Can. J. For. Res. 32(8):1420-1424. https://doi.org/10.1139/x02-068

Finney MA, 2004. FARSITE: Fire Area Simulator-model development and evaluation. USDA

Forest Service, Rocky Mountain Research Station Research Paper RMRS-RP-4 Revised. (Ogden, UT)

Finney MA, 2006. An overview of FlamMap fire modeling capabilities. Fuels Manag to Meas Success Conf Proc 213-220.

Forthofer J, Butler B, 2007. Differences in simulated fire spread over Askervein Hill using two advanced wind models and a traditional uniform wind field. In In: Butler, Bret W.; Cook, Wayne, Comps. The Fire Environment--Innovations, Management, and Policy; Conference Proceedings. 26-30 March 2007; Destin, FL. Proceedings RMRS-P-46CD. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mounta.

Forthofer JM, 2007. Modeling wind in complex terrain for use in fire spread prediction. Fort Collins, CO: Colorado State University, Thesis, 123 p.

Güngöroglu C, Güney CO, Sari A, Serttaş A, 2018. Predicting crown fuel biomass of Turkish red pine (Pinus brutia Ten.) for the Mediterranean regions of Turkey. Sumar List doi:10.31298/sl.142.11-12.4. https://doi.org/10.31298/sl.142.11-12.4

Hines F, Tolhurst KG, Wilson AAG, McCarthy GJ, 2010. Overall fuel hazard assessment guide. 4th edn. Department of Sustainability and Environment, Fire and Adaptive Management Report Number 82. (Melbourne), Australia.

Jahdi R, Darvishsefat AA, Etemad V, Mostafavi MA, 2014. Wind effect on wildfire and simulation of its spread (Case study: Siahkal forest in northern Iran). J Agric Sci Technol 16: 1109-1121.

Jahdi R, Salis M, Alcasena FJ, Arabi M, Arca B, Duce P, 2020. Evaluating landscape-scale wildfire exposure in northwestern Iran. Nat Hazards 1-22. https://doi.org/10.1007/s11069-020-03901-4

Jahdi R, Salis M, Darvishsefat AA, Alcasena F, Mostafavi MA, Etemad V, Lozano OM, Spano D, 2016. Evaluating fire modelling systems in recent wildfires of the Golestan National Park, Iran. Forestry 89: 136-149. https://doi.org/10.1093/forestry/cpv045

Jahdi R, Salis M, Darvishsefat AA, Mostafavi MA, Alcasena F, Etemad V, Lozano O, Spano D, 2015. Calibration of FARSITE simulator in northern Iranian forests. Nat Hazards Earth Syst Sci 15: 443-459, doi:10.5194/nhess-15-443-2015. https://doi.org/10.5194/nhess-15-443-2015

Keane RE, 2015. Wildland fuel fundamentals and applications. New York, NY, USA:: Springer International Publishing. (pp. 1-191). https://doi.org/10.1007/978-3-319-09015-3

Keane RE, McKenzie D, Falk DA, Smithwick EAH, Miller C, Kellogg L-KB, 2015. Representing climate, disturbance, and vegetation interactions in landscape models. Ecol Modell 309: 33-47. https://doi.org/10.1016/j.ecolmodel.2015.04.009

Keane RE, Reinhardt ED, Scott J, Gray K, Reardon J, 2005. Estimating forest canopy bulk density using six indirect methods. Can J For Res 35: 724-739. https://doi.org/10.1139/x04-213

Keeley JE, Fotheringham CJ, Moritz MA, 2004. Lessons from the october 2003. Wildfires in Southern California. J For 102: 26-31.

Lutes DC, Keane RE, 2006. Fuel Load (FL). In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. FL-1-25, 164. https://doi.org/10.2737/RMRS-GTR-164

Nalder IA, Wein RW, 1999. Long‐term forest floor carbon dynamics after fire in upland boreal forests of western Canada. Global Biogeochem Cycles 13: 951-968. https://doi.org/10.1029/1999GB900056

National Wildfire Coordinating Group (NWCG), 2019. Fire Behavior Field Reference Guide, PMS 437. Modified April 7, 2019. https://www.nwcg.gov/publications/pms437

Norum RA, Miller M, 1984. Measuring fuel moisture content in Alaska: standard methods and procedures. General Technical Report, PNW-171. U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station, 34 pp. https://doi.org/10.2737/PNW-GTR-171

Ottmar RD, Sandberg DV, Riccardi CL, Prichard SJ, 2007. An overview of the fuel characteristic classification system-quantifying, classifying, and creating fuelbeds for resource planning. Can J For Res 37: 2383-2393. https://doi.org/10.1139/X07-077

Paysen TE, Ansley RJ, Brown JK, Gottfried GJ, Haase SM, Harrington MG, Narog MG, Sackett SS, Wilson RC, 2000. Fire in western shrubland, woodland, and grassland ecosystems. Wildl fire Ecosyst Eff fire flora 2: 121-159.

Pierce KB, Ohmann JL, Wimberly MC, Gregory MJ, Fried JS, 2009. Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Can J For Res 39: 1901-1916. https://doi.org/10.1139/X09-102

Poulos HM, 2009. Mapping fuels in the Chihuahuan Desert borderlands using remote sensing, geographic information systems, and biophysical modeling. Can J For Res 39: 1917-1927. https://doi.org/10.1139/X09-100

Poulos HM, Camp AE, Gatewood RG, Loomis L, 2007. A hierarchical approach for scaling forest inventory and fuels data from local to landscape scales in the Davis Mountains, Texas, USA. Forest Ecology and Management, 244(1-3), pp.1-15. https://doi.org/10.1016/j.foreco.2007.03.033

Rebain SA, Reinhardt ED, Crookston NL, Beukema SJ, Kurz WA, Greenough JA, Robinson DCE, Lutes DC, 2010. The fire and fuels extension to the forest vegetation simulator: updated model documentation. USDA For Serv Int Rep 408.

Reich RM, Lundquist JE, Bravo VA, 2004. Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. Int J Wildl Fire 13: 119-129. https://doi.org/10.1071/WF02049

Rossi JL, Chatelon FJ, Marcelli T, 2019. Fire Intensity. In: Manzello S. (eds) Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires. Springer, Cham. https://doi.org/10.1007/978-3-319-51727-8_51-1

Sağlam B, Küçük Ö, Bilgili E, Durmaz BD, Baysal İ, 2008. Estimating fuel biomass of some shrub species (Maquis) in Turkey. Turkish J Agric For 32: 349-356.

Salis M, Arca B, Alcasena F, Arianoutsou M, Bacciu V, Duce P, Duguy B, Koutsias N, Mallinis G, Mitsopoulos I, Moreno JM., 2016. Predicting wildfire spread and behaviour in Mediterranean landscapes. Int J Wildl Fire 25. 1015-1032. https://doi.org/10.1071/WF15081

Salis M, Del Giudice L, Arca B, Ager AA, Alcasena-Urdiroz F, Lozano O, Bacciu V, Spano D, Duce P, 2018 Modeling the effects of different fuel treatment mosaics on wildfire spread and behavior in a Mediterranean agro-pastoral area. J Envir Manag 212: 490-505. https://doi.org/10.1016/j.jenvman.2018.02.020

Santoni P, Sullivan A, Morvan D, Mell WE, 2011. The Latest Advances Tools for Understanding and Managing Wildland Fire. Forest Fire Research, Article ID 418756. https://doi.org/10.1155/2011/418756

Scott JH, Burgan RE, 2005. Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel's Surface Fire Spread Model. General Technical Report RMRS-GTR-153. Fort Collins, Colorado: U.S. Forest Service, Rocky Mountain Research Station. June. https://doi.org/10.2737/RMRS-GTR-153

Scott JH, Reinhardt ED, 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. Res Pap RMRS-RP-29 Fort Collins, CO US Dep Agric For Serv Rocky Mt Res Station 59 pp 29. https://doi.org/10.2737/RMRS-RP-29

Scott JH, Reinhardt ED, 2005. Stereo photo guide for estimating canopy fuel characteristics in conifer stands. Gen Tech Rep RMRS-GTR-145 Fort Collins, CO US Dep Agric For Serv Rocky Mt Res Station 49 pp plus stereoscope (available only with Pap copy) 145. https://doi.org/10.2737/RMRS-GTR-145

Sexton, T. 2006. US Federal fuel management programs: reducing risk to communities and increasing ecosystem resilience and sustainability. In In: Andrews, Patricia L.; Butler, Bret W., comps. Fuels Management-How to Measure Success: Conference Proceedings. 28-30 March 2006; Portland, OR. Proceedings RMRS-P-41. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. pp. 9-12.

Sorenson T, 1948. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content. K Dan Vidensk Selsk Biol Skr 5: 1-34.

Stratton, RD, 2004. Assessing the effectiveness of landscape fuel treatments on fire growth and behavior', J Forestry, 102(7), pp. 32-40.

Taylor SW, Woolford DG, Dean CB, Martell DL, 2013. Wildfire prediction to inform management: statistical science challenges. Stat Sci 586-615. https://doi.org/10.1214/13-STS451

Vega-Garcia C, Duguy B, Monfort IP, Costafreda-Aumedes, S, 2014. Characterization of custom fuel models for supporting fire modeling-based optimization of prescribed fire planning in relation to wildfire prevention (southern Catalonia, Spain). II International Conference on Forest Fire Research At: Coimbra, Portugal, Volume: 4. https://doi.org/10.14195/978-989-26-0884-6_12

Wu ZW, He HS, Chang Y, Liu ZH, Chen HW, 2011. Development of customized fire behavior fuel models for boreal forests of Northeastern China. Environ Manage 48: 1148-1157. https://doi.org/10.1007/s00267-011-9707-3

Xanthopoulos G, Athanasiou M, 2020. Crown Fire', Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, pp. 1-15. https://doi.org/10.1007/978-3-319-51727-8_13-1

Silva FR, Molina-Martínez JR, 2012. Modeling Mediterranean forest fuels by integrating field data and mapping tools. Eur J For Res 131: 571-582. https://doi.org/10.1007/s10342-011-0532-2

Yavuz M, Sağlam B, Küçük Ö, Tüfekçioğlu A, 2018. Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu Üniversitesi Orman Fakültesi Derg 171-188. https://doi.org/10.17475/kastorman.459698

Published
2021-08-03
How to Cite
Alhaj-KhalafM. W., Shataee JoibaryS., JahdiR., & BacciuV. (2021). Improved forest fire spread mapping by developing custom fire fuel models in replanted forests in Hyrcanian forests, Iran. Forest Systems, 30(2), e008. https://doi.org/10.5424/fs/2021302-17980
Section
Research Articles